Content deleted Content added
→Libraries: removed unenlightening Python code |
Fix typo, minor rephrasing Tags: Mobile edit Mobile web edit |
||
Line 2:
{{Distinguish|recursive neural network}}
{{Machine learning|Artificial neural network}}
'''Recurrent neural networks''' ('''RNNs''') are a class of [[Neural network (machine learning)|artificial neural networks]] for sequential data processing. Unlike [[feedforward neural network]]s, which process data in a single pass, RNNs process data across multiple time steps, making them well-adapted for modelling and processing text, speech, and [[time series]].<ref>{{Cite journal |last1=Tealab |first1=Ahmed |date=2018-12-01 |title=Time series forecasting using artificial neural networks methodologies: A systematic review |journal=Future Computing and Informatics Journal |volume=3 |issue=2 |pages=334–340 |doi=10.1016/j.fcij.2018.10.003 |issn=2314-7288 |doi-access=free}}</ref>▼
▲'''Recurrent neural networks''' ('''RNNs''') are a class of [[Neural network (machine learning)|artificial neural
The fundamental building block of an RNN is the recurrent unit. This unit maintains a hidden state, essentially a form of memory, which is updated at each time step based on the current input and the previous hidden state. This feedback loop allows the network to learn from past inputs and incorporate that knowledge into its current processing.▼
▲The
Early RNNs suffered from the vanishing gradient problem, limiting their ability to learn long-range dependencies. This was solved by the invention of [[Long short-term memory]] (LSTM) networks in 1997, which became the standard architecture for RNN.▼
▲Early RNNs suffered from the [[vanishing gradient problem]], limiting their ability to learn long-range dependencies. This was solved by the
They have been applied to tasks such as unsegmented, connected [[handwriting recognition]],<ref>{{cite journal |last1=Graves |first1=Alex |author-link1=Alex Graves (computer scientist) |last2=Liwicki |first2=Marcus |last3=Fernandez |first3=Santiago |last4=Bertolami |first4=Roman |last5=Bunke |first5=Horst |last6=Schmidhuber |first6=Jürgen |author-link6=Jürgen Schmidhuber |title=A Novel Connectionist System for Improved Unconstrained Handwriting Recognition |url=http://www.idsia.ch/~juergen/tpami_2008.pdf |journal=IEEE Transactions on Pattern Analysis and Machine Intelligence |volume=31 |issue=5 |pages=855–868 |year=2009 |doi=10.1109/tpami.2008.137 |pmid=19299860 |citeseerx=10.1.1.139.4502 |s2cid=14635907 }}</ref> [[speech recognition]],<ref name="sak2014">{{Cite web |url=https://research.google.com/pubs/archive/43905.pdf |publisher=Google Research |title=Long Short-Term Memory recurrent neural network architectures for large scale acoustic modeling |last1=Sak |first1=Haşim |last2=Senior |first2=Andrew |last3=Beaufays | first3=Françoise |year=2014 }}</ref><ref name="liwu2015">{{cite arXiv |last1=Li |first1=Xiangang |last2=Wu |first2=Xihong |date=2014-10-15 |title=Constructing Long Short-Term Memory based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognition |eprint=1410.4281 |class=cs.CL }}</ref> [[natural language processing]], and [[neural machine translation]].<ref>{{Cite journal |last=Dupond |first=Samuel |date=2019 |title=<!-- for sure correct title? not found, nor in archive.org (for 2020-02-13), nor Volume correct? 2019 is vol 47-48 and 41 from 2016--> A thorough review on the current advance of neural network structures. |url=https://www.sciencedirect.com/journal/annual-reviews-in-control |journal=Annual Reviews in Control |volume=14 |pages=200–230}}</ref><ref>{{Cite journal |last1=Abiodun |first1=Oludare Isaac |last2=Jantan |first2=Aman |last3=Omolara |first3=Abiodun Esther |last4=Dada |first4=Kemi Victoria |last5=Mohamed |first5=Nachaat Abdelatif |last6=Arshad |first6=Humaira |date=2018-11-01 |title=State-of-the-art in artificial neural network applications: A survey |journal=Heliyon |volume=4 |issue=11 |pages=e00938 |bibcode=2018Heliy...400938A |doi=10.1016/j.heliyon.2018.e00938 |issn=2405-8440 |pmc=6260436 |pmid=30519653 |doi-access=free}}</ref>▼
▲
{{toclimit|3}}
| |||